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<h1 id="sec_name">
<span data-if="hdevelop" style="display:inline;">create_dl_model_detection</span><span data-if="c" style="display:none;">T_create_dl_model_detection</span><span data-if="cpp" style="display:none;">CreateDlModelDetection</span><span data-if="dotnet" style="display:none;">CreateDlModelDetection</span><span data-if="python" style="display:none;">create_dl_model_detection</span> (算子名称)</h1>
<h2>名称</h2>
<p><code><span data-if="hdevelop" style="display:inline;">create_dl_model_detection</span><span data-if="c" style="display:none;">T_create_dl_model_detection</span><span data-if="cpp" style="display:none;">CreateDlModelDetection</span><span data-if="dotnet" style="display:none;">CreateDlModelDetection</span><span data-if="python" style="display:none;">create_dl_model_detection</span></code> — Create a deep learning network for object detection or instance segmentation.</p>
<h2 id="sec_synopsis">参数签名</h2>
<div data-if="hdevelop" style="display:inline;">
<p>
<code><b>create_dl_model_detection</b>( :  : <a href="#Backbone"><i>Backbone</i></a>, <a href="#NumClasses"><i>NumClasses</i></a>, <a href="#DLModelDetectionParam"><i>DLModelDetectionParam</i></a> : <a href="#DLModelHandle"><i>DLModelHandle</i></a>)</code></p>
</div>
<div data-if="c" style="display:none;">
<p>
<code>Herror <b>T_create_dl_model_detection</b>(const Htuple <a href="#Backbone"><i>Backbone</i></a>, const Htuple <a href="#NumClasses"><i>NumClasses</i></a>, const Htuple <a href="#DLModelDetectionParam"><i>DLModelDetectionParam</i></a>, Htuple* <a href="#DLModelHandle"><i>DLModelHandle</i></a>)</code></p>
</div>
<div data-if="cpp" style="display:none;">
<p>
<code>void <b>CreateDlModelDetection</b>(const HTuple&amp; <a href="#Backbone"><i>Backbone</i></a>, const HTuple&amp; <a href="#NumClasses"><i>NumClasses</i></a>, const HTuple&amp; <a href="#DLModelDetectionParam"><i>DLModelDetectionParam</i></a>, HTuple* <a href="#DLModelHandle"><i>DLModelHandle</i></a>)</code></p>
<p>
<code>void <a href="HDlModel.html">HDlModel</a>::<b>HDlModel</b>(const HString&amp; <a href="#Backbone"><i>Backbone</i></a>, Hlong <a href="#NumClasses"><i>NumClasses</i></a>, const HDict&amp; <a href="#DLModelDetectionParam"><i>DLModelDetectionParam</i></a>)</code></p>
<p>
<code>void <a href="HDlModel.html">HDlModel</a>::<b>HDlModel</b>(const char* <a href="#Backbone"><i>Backbone</i></a>, Hlong <a href="#NumClasses"><i>NumClasses</i></a>, const HDict&amp; <a href="#DLModelDetectionParam"><i>DLModelDetectionParam</i></a>)</code></p>
<p>
<code>void <a href="HDlModel.html">HDlModel</a>::<b>HDlModel</b>(const wchar_t* <a href="#Backbone"><i>Backbone</i></a>, Hlong <a href="#NumClasses"><i>NumClasses</i></a>, const HDict&amp; <a href="#DLModelDetectionParam"><i>DLModelDetectionParam</i></a>)  <span class="signnote">
            (
            Windows only)
          </span></code></p>
<p>
<code>void <a href="HDlModel.html">HDlModel</a>::<b>CreateDlModelDetection</b>(const HString&amp; <a href="#Backbone"><i>Backbone</i></a>, Hlong <a href="#NumClasses"><i>NumClasses</i></a>, const HDict&amp; <a href="#DLModelDetectionParam"><i>DLModelDetectionParam</i></a>)</code></p>
<p>
<code>void <a href="HDlModel.html">HDlModel</a>::<b>CreateDlModelDetection</b>(const char* <a href="#Backbone"><i>Backbone</i></a>, Hlong <a href="#NumClasses"><i>NumClasses</i></a>, const HDict&amp; <a href="#DLModelDetectionParam"><i>DLModelDetectionParam</i></a>)</code></p>
<p>
<code>void <a href="HDlModel.html">HDlModel</a>::<b>CreateDlModelDetection</b>(const wchar_t* <a href="#Backbone"><i>Backbone</i></a>, Hlong <a href="#NumClasses"><i>NumClasses</i></a>, const HDict&amp; <a href="#DLModelDetectionParam"><i>DLModelDetectionParam</i></a>)  <span class="signnote">
            (
            Windows only)
          </span></code></p>
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<p>
<code>static void <a href="HOperatorSet.html">HOperatorSet</a>.<b>CreateDlModelDetection</b>(<a href="HTuple.html">HTuple</a> <a href="#Backbone"><i>backbone</i></a>, <a href="HTuple.html">HTuple</a> <a href="#NumClasses"><i>numClasses</i></a>, <a href="HTuple.html">HTuple</a> <a href="#DLModelDetectionParam"><i>DLModelDetectionParam</i></a>, out <a href="HTuple.html">HTuple</a> <a href="#DLModelHandle"><i>DLModelHandle</i></a>)</code></p>
<p>
<code>public <a href="HDlModel.html">HDlModel</a>(string <a href="#Backbone"><i>backbone</i></a>, int <a href="#NumClasses"><i>numClasses</i></a>, <a href="HDict.html">HDict</a> <a href="#DLModelDetectionParam"><i>DLModelDetectionParam</i></a>)</code></p>
<p>
<code>void <a href="HDlModel.html">HDlModel</a>.<b>CreateDlModelDetection</b>(string <a href="#Backbone"><i>backbone</i></a>, int <a href="#NumClasses"><i>numClasses</i></a>, <a href="HDict.html">HDict</a> <a href="#DLModelDetectionParam"><i>DLModelDetectionParam</i></a>)</code></p>
</div>
<div data-if="python" style="display:none;">
<p>
<code>def <b>create_dl_model_detection</b>(<a href="#Backbone"><i>backbone</i></a>: str, <a href="#NumClasses"><i>num_classes</i></a>: int, <a href="#DLModelDetectionParam"><i>dlmodel_detection_param</i></a>: HHandle) -&gt; HHandle</code></p>
</div>
<h2 id="sec_description">描述</h2>
<p>With 该算子 <code><span data-if="hdevelop" style="display:inline">create_dl_model_detection</span><span data-if="c" style="display:none">create_dl_model_detection</span><span data-if="cpp" style="display:none">CreateDlModelDetection</span><span data-if="com" style="display:none">CreateDlModelDetection</span><span data-if="dotnet" style="display:none">CreateDlModelDetection</span><span data-if="python" style="display:none">create_dl_model_detection</span></code> a deep learning network
for object detection or instance segmentation is created.
See the chapter <a href="toc_deeplearning_objectdetection.html">Deep Learning / Object Detection and Instance Segmentation</a> for further
information on object detection and instance segmentation based on
deep learning.
The handle of this network is returned in <a href="#DLModelHandle"><i><code><span data-if="hdevelop" style="display:inline">DLModelHandle</span><span data-if="c" style="display:none">DLModelHandle</span><span data-if="cpp" style="display:none">DLModelHandle</span><span data-if="com" style="display:none">DLModelHandle</span><span data-if="dotnet" style="display:none">DLModelHandle</span><span data-if="python" style="display:none">dlmodel_handle</span></code></i></a>.
</p>
<p>You can specify your model and its architecture over the parameters listed
below.
To successfully create a detection model, you need to specify its backbone
and the number of classes the model shall be able to distinguish.
The first information is handed over through the parameter
<a href="#Backbone"><i><code><span data-if="hdevelop" style="display:inline">Backbone</span><span data-if="c" style="display:none">Backbone</span><span data-if="cpp" style="display:none">Backbone</span><span data-if="com" style="display:none">Backbone</span><span data-if="dotnet" style="display:none">backbone</span><span data-if="python" style="display:none">backbone</span></code></i></a> which is explained below in the section
“Possible Backbones”.
The second information is given through the parameter <a href="#NumClasses"><i><code><span data-if="hdevelop" style="display:inline">NumClasses</span><span data-if="c" style="display:none">NumClasses</span><span data-if="cpp" style="display:none">NumClasses</span><span data-if="com" style="display:none">NumClasses</span><span data-if="dotnet" style="display:none">numClasses</span><span data-if="python" style="display:none">num_classes</span></code></i></a>.
Note, this parameter fixes the number of classes the network will
distinguish and therewith also the number of entries in
<i><span data-if="hdevelop" style="display:inline">'class_ids'</span><span data-if="c" style="display:none">"class_ids"</span><span data-if="cpp" style="display:none">"class_ids"</span><span data-if="com" style="display:none">"class_ids"</span><span data-if="dotnet" style="display:none">"class_ids"</span><span data-if="python" style="display:none">"class_ids"</span></i> and <i><span data-if="hdevelop" style="display:inline">'class_names'</span><span data-if="c" style="display:none">"class_names"</span><span data-if="cpp" style="display:none">"class_names"</span><span data-if="com" style="display:none">"class_names"</span><span data-if="dotnet" style="display:none">"class_names"</span><span data-if="python" style="display:none">"class_names"</span></i>.
</p>
<p>The values of all other applicable parameters can be specified using
the dictionary <a href="#DLModelDetectionParam"><i><code><span data-if="hdevelop" style="display:inline">DLModelDetectionParam</span><span data-if="c" style="display:none">DLModelDetectionParam</span><span data-if="cpp" style="display:none">DLModelDetectionParam</span><span data-if="com" style="display:none">DLModelDetectionParam</span><span data-if="dotnet" style="display:none">DLModelDetectionParam</span><span data-if="python" style="display:none">dlmodel_detection_param</span></code></i></a>.
Such a parameter is e.g., the <i><span data-if="hdevelop" style="display:inline">'instance_type'</span><span data-if="c" style="display:none">"instance_type"</span><span data-if="cpp" style="display:none">"instance_type"</span><span data-if="com" style="display:none">"instance_type"</span><span data-if="dotnet" style="display:none">"instance_type"</span><span data-if="python" style="display:none">"instance_type"</span></i>, determining which
kind of bounding boxes the model handles.
To create a deep learning network for instance segmentation the parameter
<i><span data-if="hdevelop" style="display:inline">'instance_segmentation'</span><span data-if="c" style="display:none">"instance_segmentation"</span><span data-if="cpp" style="display:none">"instance_segmentation"</span><span data-if="com" style="display:none">"instance_segmentation"</span><span data-if="dotnet" style="display:none">"instance_segmentation"</span><span data-if="python" style="display:none">"instance_segmentation"</span></i> has to be set to <i><span data-if="hdevelop" style="display:inline">'true'</span><span data-if="c" style="display:none">"true"</span><span data-if="cpp" style="display:none">"true"</span><span data-if="com" style="display:none">"true"</span><span data-if="dotnet" style="display:none">"true"</span><span data-if="python" style="display:none">"true"</span></i>.
The full list of parameters that can be set is given below in the section
“Settable Parameters”. Some parameters are only available for instance
segmentation.
In case a parameter is not specified, the default value is taken to create
the model.
Note, parameters influencing the network architecture will not be changeable
anymore once the network has been created.
All the other parameters can still be set or changed using 该算子
<a href="set_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">set_dl_model_param</span><span data-if="c" style="display:none">set_dl_model_param</span><span data-if="cpp" style="display:none">SetDlModelParam</span><span data-if="com" style="display:none">SetDlModelParam</span><span data-if="dotnet" style="display:none">SetDlModelParam</span><span data-if="python" style="display:none">set_dl_model_param</span></code></a>.
An overview, how parameters can be set is given in
<a href="get_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">get_dl_model_param</span><span data-if="c" style="display:none">get_dl_model_param</span><span data-if="cpp" style="display:none">GetDlModelParam</span><span data-if="com" style="display:none">GetDlModelParam</span><span data-if="dotnet" style="display:none">GetDlModelParam</span><span data-if="python" style="display:none">get_dl_model_param</span></code></a>, where also a description of the specific
parameters is provided.
After creating the object detection model, the <i><span data-if="hdevelop" style="display:inline">'type'</span><span data-if="c" style="display:none">"type"</span><span data-if="cpp" style="display:none">"type"</span><span data-if="com" style="display:none">"type"</span><span data-if="dotnet" style="display:none">"type"</span><span data-if="python" style="display:none">"type"</span></i> will
automatically be set to <i><span data-if="hdevelop" style="display:inline">'detection'</span><span data-if="c" style="display:none">"detection"</span><span data-if="cpp" style="display:none">"detection"</span><span data-if="com" style="display:none">"detection"</span><span data-if="dotnet" style="display:none">"detection"</span><span data-if="python" style="display:none">"detection"</span></i>.
</p>
<h3>Possible Backbones</h3>
<p>The parameter <a href="#Backbone"><i><code><span data-if="hdevelop" style="display:inline">Backbone</span><span data-if="c" style="display:none">Backbone</span><span data-if="cpp" style="display:none">Backbone</span><span data-if="com" style="display:none">Backbone</span><span data-if="dotnet" style="display:none">backbone</span><span data-if="python" style="display:none">backbone</span></code></i></a> determines the backbone your
network will use. See the chapter <a href="toc_deeplearning_objectdetection.html">Deep Learning / Object Detection and Instance Segmentation</a>
for more information to the backbone. In short, the backbone consists of a
pretrained classifier, from which only the layers necessary to generate
the feature maps are kept.
Hence, there are no fully connected layers anymore in the network.
This implies that you read in a classifier as feature extractor for
the subsequent detection network.
For this you can read in a classifier in the HALCON format or a model
in the ONNX format, see <a href="read_dl_model.html"><code><span data-if="hdevelop" style="display:inline">read_dl_model</span><span data-if="c" style="display:none">read_dl_model</span><span data-if="cpp" style="display:none">ReadDlModel</span><span data-if="com" style="display:none">ReadDlModel</span><span data-if="dotnet" style="display:none">ReadDlModel</span><span data-if="python" style="display:none">read_dl_model</span></code></a> for more information.
</p>
<p><code><span data-if="hdevelop" style="display:inline">create_dl_model_detection</span><span data-if="c" style="display:none">create_dl_model_detection</span><span data-if="cpp" style="display:none">CreateDlModelDetection</span><span data-if="com" style="display:none">CreateDlModelDetection</span><span data-if="dotnet" style="display:none">CreateDlModelDetection</span><span data-if="python" style="display:none">create_dl_model_detection</span></code> attaches the feature pyramid on
different levels of the backbone.
More precisely, the backbone has for different levels a layer specified
as docking layer. When creating a detection
model, the feature pyramid is attached on the corresponding docking layer.
The pretrained classifiers provided by HALCON have already
specified docking layers. But when you use a self-provided classifier as
backbone, you have to specify them yourself.
You can set <code>backbone_docking_layers</code> as part of the classifier
using 该算子 <a href="set_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">set_dl_model_param</span><span data-if="c" style="display:none">set_dl_model_param</span><span data-if="cpp" style="display:none">SetDlModelParam</span><span data-if="com" style="display:none">SetDlModelParam</span><span data-if="dotnet" style="display:none">SetDlModelParam</span><span data-if="python" style="display:none">set_dl_model_param</span></code></a> or the backbone as such
using this operator.
</p>
<p>The docking layers are from different levels and therefore the feature
maps used in the feature pyramid are of different size.
More precisely, in the feature pyramid the feature map lengths are halved
with every level.
By implication, the input image lengths need to be halved for every level.
This means, the network architectures allow changes concerning the image
dimensions, but the dimensions <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i> and
<i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>
need to be an integer multiple of
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</svg></span> is the highest level up to which the feature
pyramid is built.
This value depends on the attached networks as well as on the docking
layers. For the provided classifiers the list below mentions, up to which
levels the feature pyramid is built using default settings.
</p>
<p>HALCON provides the following pretrained classifiers you can read in
as backbone:
</p>
<dl class="generic">


<dt><b><i><span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_alexnet.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_alexnet.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_alexnet.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_alexnet.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_alexnet.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_alexnet.hdl"</span></i>:</b></dt>
<dd>
<p>

This neural network is designed for simple classification tasks.
It is characterized by its convolution kernels in the first
convolution layers, which are larger than in other networks with
comparable classification performance
(e.g., <i><span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_compact.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_compact.hdl"</span></i>).
This may be beneficial for feature extraction.
</p>
<p>This backbone expects the images to be of the type <code>real</code>.
Additionally, the backbone is designed for certain image properties.
The corresponding values can be retrieved with
<a href="get_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">get_dl_model_param</span><span data-if="c" style="display:none">get_dl_model_param</span><span data-if="cpp" style="display:none">GetDlModelParam</span><span data-if="com" style="display:none">GetDlModelParam</span><span data-if="dotnet" style="display:none">GetDlModelParam</span><span data-if="python" style="display:none">get_dl_model_param</span></code></a>. Here we list the default values with
which the classifier has been trained:
</p>
<dl class="generic">

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i>: 224
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>: 224
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_num_channels'</span><span data-if="c" style="display:none">"image_num_channels"</span><span data-if="cpp" style="display:none">"image_num_channels"</span><span data-if="com" style="display:none">"image_num_channels"</span><span data-if="dotnet" style="display:none">"image_num_channels"</span><span data-if="python" style="display:none">"image_num_channels"</span></i>: 3
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_min'</span><span data-if="c" style="display:none">"image_range_min"</span><span data-if="cpp" style="display:none">"image_range_min"</span><span data-if="com" style="display:none">"image_range_min"</span><span data-if="dotnet" style="display:none">"image_range_min"</span><span data-if="python" style="display:none">"image_range_min"</span></i>: -127.0
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_max'</span><span data-if="c" style="display:none">"image_range_max"</span><span data-if="cpp" style="display:none">"image_range_max"</span><span data-if="com" style="display:none">"image_range_max"</span><span data-if="dotnet" style="display:none">"image_range_max"</span><span data-if="python" style="display:none">"image_range_max"</span></i>: 128.0
</p></dd>
</dl>

<p>The default feature pyramid built on this backbone goes up to level 4.
</p>
</dd>

<dt><b><i><span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_compact.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_compact.hdl"</span></i>:</b></dt>
<dd>
<p>

This neural network is designed to be memory and runtime efficient.
</p>
<p>This backbone expects the images to be of the type <code>real</code>.
Additionally, the backbone is designed for certain image properties.
The corresponding values can be retrieved with
<a href="get_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">get_dl_model_param</span><span data-if="c" style="display:none">get_dl_model_param</span><span data-if="cpp" style="display:none">GetDlModelParam</span><span data-if="com" style="display:none">GetDlModelParam</span><span data-if="dotnet" style="display:none">GetDlModelParam</span><span data-if="python" style="display:none">get_dl_model_param</span></code></a>. Here we list the default values with
which the classifier has been trained:
</p>
<dl class="generic">

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i>: 224
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>: 224
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_num_channels'</span><span data-if="c" style="display:none">"image_num_channels"</span><span data-if="cpp" style="display:none">"image_num_channels"</span><span data-if="com" style="display:none">"image_num_channels"</span><span data-if="dotnet" style="display:none">"image_num_channels"</span><span data-if="python" style="display:none">"image_num_channels"</span></i>: 3
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_min'</span><span data-if="c" style="display:none">"image_range_min"</span><span data-if="cpp" style="display:none">"image_range_min"</span><span data-if="com" style="display:none">"image_range_min"</span><span data-if="dotnet" style="display:none">"image_range_min"</span><span data-if="python" style="display:none">"image_range_min"</span></i>: -127.0
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_max'</span><span data-if="c" style="display:none">"image_range_max"</span><span data-if="cpp" style="display:none">"image_range_max"</span><span data-if="com" style="display:none">"image_range_max"</span><span data-if="dotnet" style="display:none">"image_range_max"</span><span data-if="python" style="display:none">"image_range_max"</span></i>: 128.0
</p></dd>
</dl>

<p>The default feature pyramid built on this backbone goes up to level 4.
</p>
</dd>

<dt><b><i><span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_enhanced.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span></i>:</b></dt>
<dd>
<p>

This neural network has more hidden layers than
<i><span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_compact.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_compact.hdl"</span></i> and is therefore
assumed to be better suited for more complex tasks. But this comes
at the cost of being more time and memory demanding.
</p>
<p>This backbone expects the images to be of the type <code>real</code>.
Additionally, the backbone is designed for certain image properties.
The corresponding values can be retrieved with
<a href="get_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">get_dl_model_param</span><span data-if="c" style="display:none">get_dl_model_param</span><span data-if="cpp" style="display:none">GetDlModelParam</span><span data-if="com" style="display:none">GetDlModelParam</span><span data-if="dotnet" style="display:none">GetDlModelParam</span><span data-if="python" style="display:none">get_dl_model_param</span></code></a>. Here we list the default values with
which the classifier has been trained:
</p>
<dl class="generic">

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i>: 224
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>: 224
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_num_channels'</span><span data-if="c" style="display:none">"image_num_channels"</span><span data-if="cpp" style="display:none">"image_num_channels"</span><span data-if="com" style="display:none">"image_num_channels"</span><span data-if="dotnet" style="display:none">"image_num_channels"</span><span data-if="python" style="display:none">"image_num_channels"</span></i>: 3
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_min'</span><span data-if="c" style="display:none">"image_range_min"</span><span data-if="cpp" style="display:none">"image_range_min"</span><span data-if="com" style="display:none">"image_range_min"</span><span data-if="dotnet" style="display:none">"image_range_min"</span><span data-if="python" style="display:none">"image_range_min"</span></i>: -127.0
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_max'</span><span data-if="c" style="display:none">"image_range_max"</span><span data-if="cpp" style="display:none">"image_range_max"</span><span data-if="com" style="display:none">"image_range_max"</span><span data-if="dotnet" style="display:none">"image_range_max"</span><span data-if="python" style="display:none">"image_range_max"</span></i>: 128.0
</p></dd>
</dl>

<p>The default feature pyramid built on this backbone goes up to level 5.
</p>
</dd>

<dt><b><i><span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_mobilenet_v2.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_mobilenet_v2.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_mobilenet_v2.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_mobilenet_v2.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_mobilenet_v2.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_mobilenet_v2.hdl"</span></i>:</b></dt>
<dd>
<p>

This classifier is a small and low-power model, and hence it is more
suitable for mobile and embedded vision applications.
</p>
<p>This backbone expects the images to be of the type <code>real</code>.
Additionally, the backbone is designed for certain image properties.
The corresponding values can be retrieved with
<a href="get_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">get_dl_model_param</span><span data-if="c" style="display:none">get_dl_model_param</span><span data-if="cpp" style="display:none">GetDlModelParam</span><span data-if="com" style="display:none">GetDlModelParam</span><span data-if="dotnet" style="display:none">GetDlModelParam</span><span data-if="python" style="display:none">get_dl_model_param</span></code></a>. Here we list the default values with
which the classifier has been trained:
</p>
<dl class="generic">

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i>: 224
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>: 224
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_num_channels'</span><span data-if="c" style="display:none">"image_num_channels"</span><span data-if="cpp" style="display:none">"image_num_channels"</span><span data-if="com" style="display:none">"image_num_channels"</span><span data-if="dotnet" style="display:none">"image_num_channels"</span><span data-if="python" style="display:none">"image_num_channels"</span></i>: 3
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_min'</span><span data-if="c" style="display:none">"image_range_min"</span><span data-if="cpp" style="display:none">"image_range_min"</span><span data-if="com" style="display:none">"image_range_min"</span><span data-if="dotnet" style="display:none">"image_range_min"</span><span data-if="python" style="display:none">"image_range_min"</span></i>: -127.0
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_max'</span><span data-if="c" style="display:none">"image_range_max"</span><span data-if="cpp" style="display:none">"image_range_max"</span><span data-if="com" style="display:none">"image_range_max"</span><span data-if="dotnet" style="display:none">"image_range_max"</span><span data-if="python" style="display:none">"image_range_max"</span></i>: 128.0
</p></dd>
</dl>

<p>The default feature pyramid built on this backbone goes up to level 4.
</p>
</dd>

<dt><b><i><span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_resnet18.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_resnet18.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_resnet18.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_resnet18.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_resnet18.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_resnet18.hdl"</span></i>:</b></dt>
<dd>
<p>

As the network <i><span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_enhanced.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span></i>,
this network is suited for more complex tasks.
But its structure differs, bringing the advantage of making the
training more stable and being internally more robust. Compared to
the neural network <i><span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_resnet50.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span></i>
it is less complex and has faster inference times.
</p>
<p>This backbone expects the images to be of the type <code>real</code>.
Additionally, the backbone is designed for certain image properties.
The corresponding values can be retrieved with
<a href="get_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">get_dl_model_param</span><span data-if="c" style="display:none">get_dl_model_param</span><span data-if="cpp" style="display:none">GetDlModelParam</span><span data-if="com" style="display:none">GetDlModelParam</span><span data-if="dotnet" style="display:none">GetDlModelParam</span><span data-if="python" style="display:none">get_dl_model_param</span></code></a>. Here we list the default values with
which the classifier has been trained:
</p>
<dl class="generic">

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i>: 224
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>: 224
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_num_channels'</span><span data-if="c" style="display:none">"image_num_channels"</span><span data-if="cpp" style="display:none">"image_num_channels"</span><span data-if="com" style="display:none">"image_num_channels"</span><span data-if="dotnet" style="display:none">"image_num_channels"</span><span data-if="python" style="display:none">"image_num_channels"</span></i>: 3
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_min'</span><span data-if="c" style="display:none">"image_range_min"</span><span data-if="cpp" style="display:none">"image_range_min"</span><span data-if="com" style="display:none">"image_range_min"</span><span data-if="dotnet" style="display:none">"image_range_min"</span><span data-if="python" style="display:none">"image_range_min"</span></i>: -127.0
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_max'</span><span data-if="c" style="display:none">"image_range_max"</span><span data-if="cpp" style="display:none">"image_range_max"</span><span data-if="com" style="display:none">"image_range_max"</span><span data-if="dotnet" style="display:none">"image_range_max"</span><span data-if="python" style="display:none">"image_range_max"</span></i>: 128.0
</p></dd>
</dl>

<p>The default feature pyramid built on this backbone goes up to level 5.
</p>
</dd>

<dt><b><i><span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_resnet50.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span></i>:</b></dt>
<dd>
<p>

As the network <i><span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_enhanced.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span></i>,
this network is suited for more complex tasks.
But its structure differs, bringing the advantage of making the
training more stable and being internally more robust.
</p>
<p>This backbone expects the images to be of the type <code>real</code>.
Additionally, the backbone is designed for certain image properties.
The corresponding values can be retrieved with
<a href="get_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">get_dl_model_param</span><span data-if="c" style="display:none">get_dl_model_param</span><span data-if="cpp" style="display:none">GetDlModelParam</span><span data-if="com" style="display:none">GetDlModelParam</span><span data-if="dotnet" style="display:none">GetDlModelParam</span><span data-if="python" style="display:none">get_dl_model_param</span></code></a>. Here we list the default values with
which the classifier has been trained:
</p>
<dl class="generic">

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i>: 224
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>: 224
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_num_channels'</span><span data-if="c" style="display:none">"image_num_channels"</span><span data-if="cpp" style="display:none">"image_num_channels"</span><span data-if="com" style="display:none">"image_num_channels"</span><span data-if="dotnet" style="display:none">"image_num_channels"</span><span data-if="python" style="display:none">"image_num_channels"</span></i>: 3
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_min'</span><span data-if="c" style="display:none">"image_range_min"</span><span data-if="cpp" style="display:none">"image_range_min"</span><span data-if="com" style="display:none">"image_range_min"</span><span data-if="dotnet" style="display:none">"image_range_min"</span><span data-if="python" style="display:none">"image_range_min"</span></i>: -127.0
</p></dd>

<dt><b></b></dt>
<dd><p>
 <i><span data-if="hdevelop" style="display:inline">'image_range_max'</span><span data-if="c" style="display:none">"image_range_max"</span><span data-if="cpp" style="display:none">"image_range_max"</span><span data-if="com" style="display:none">"image_range_max"</span><span data-if="dotnet" style="display:none">"image_range_max"</span><span data-if="python" style="display:none">"image_range_max"</span></i>: 128.0
</p></dd>
</dl>

<p>The default feature pyramid built on this backbone goes up to level 5.
</p>
</dd>
</dl>
<h3>Settable Parameters</h3>
<p>Parameters you can set for your model when creating it using
<code><span data-if="hdevelop" style="display:inline">create_dl_model_detection</span><span data-if="c" style="display:none">create_dl_model_detection</span><span data-if="cpp" style="display:none">CreateDlModelDetection</span><span data-if="com" style="display:none">CreateDlModelDetection</span><span data-if="dotnet" style="display:none">CreateDlModelDetection</span><span data-if="python" style="display:none">create_dl_model_detection</span></code> (see <a href="get_dl_model_param.html"><code><span data-if="hdevelop" style="display:inline">get_dl_model_param</span><span data-if="c" style="display:none">get_dl_model_param</span><span data-if="cpp" style="display:none">GetDlModelParam</span><span data-if="com" style="display:none">GetDlModelParam</span><span data-if="dotnet" style="display:none">GetDlModelParam</span><span data-if="python" style="display:none">get_dl_model_param</span></code></a> for
explanations):
</p>
<ul>

<li>
<p> <i><span data-if="hdevelop" style="display:inline">'anchor_angles'</span><span data-if="c" style="display:none">"anchor_angles"</span><span data-if="cpp" style="display:none">"anchor_angles"</span><span data-if="com" style="display:none">"anchor_angles"</span><span data-if="dotnet" style="display:none">"anchor_angles"</span><span data-if="python" style="display:none">"anchor_angles"</span></i>
</p>
</li>
<li>
<p> <i><span data-if="hdevelop" style="display:inline">'anchor_aspect_ratios'</span><span data-if="c" style="display:none">"anchor_aspect_ratios"</span><span data-if="cpp" style="display:none">"anchor_aspect_ratios"</span><span data-if="com" style="display:none">"anchor_aspect_ratios"</span><span data-if="dotnet" style="display:none">"anchor_aspect_ratios"</span><span data-if="python" style="display:none">"anchor_aspect_ratios"</span></i> (legacy: <i><span data-if="hdevelop" style="display:inline">'aspect_ratios'</span><span data-if="c" style="display:none">"aspect_ratios"</span><span data-if="cpp" style="display:none">"aspect_ratios"</span><span data-if="com" style="display:none">"aspect_ratios"</span><span data-if="dotnet" style="display:none">"aspect_ratios"</span><span data-if="python" style="display:none">"aspect_ratios"</span></i>)
</p>
</li>
<li>
<p> <i><span data-if="hdevelop" style="display:inline">'anchor_num_subscales'</span><span data-if="c" style="display:none">"anchor_num_subscales"</span><span data-if="cpp" style="display:none">"anchor_num_subscales"</span><span data-if="com" style="display:none">"anchor_num_subscales"</span><span data-if="dotnet" style="display:none">"anchor_num_subscales"</span><span data-if="python" style="display:none">"anchor_num_subscales"</span></i> (legacy: <i><span data-if="hdevelop" style="display:inline">'num_subscales'</span><span data-if="c" style="display:none">"num_subscales"</span><span data-if="cpp" style="display:none">"num_subscales"</span><span data-if="com" style="display:none">"num_subscales"</span><span data-if="dotnet" style="display:none">"num_subscales"</span><span data-if="python" style="display:none">"num_subscales"</span></i>)
</p>
</li>
<li>
<p> <i><span data-if="hdevelop" style="display:inline">'backbone_docking_layers'</span><span data-if="c" style="display:none">"backbone_docking_layers"</span><span data-if="cpp" style="display:none">"backbone_docking_layers"</span><span data-if="com" style="display:none">"backbone_docking_layers"</span><span data-if="dotnet" style="display:none">"backbone_docking_layers"</span><span data-if="python" style="display:none">"backbone_docking_layers"</span></i>
</p>
</li>
<li>
<p> <i><span data-if="hdevelop" style="display:inline">'bbox_heads_weight'</span><span data-if="c" style="display:none">"bbox_heads_weight"</span><span data-if="cpp" style="display:none">"bbox_heads_weight"</span><span data-if="com" style="display:none">"bbox_heads_weight"</span><span data-if="dotnet" style="display:none">"bbox_heads_weight"</span><span data-if="python" style="display:none">"bbox_heads_weight"</span></i>, <i><span data-if="hdevelop" style="display:inline">'class_heads_weight'</span><span data-if="c" style="display:none">"class_heads_weight"</span><span data-if="cpp" style="display:none">"class_heads_weight"</span><span data-if="com" style="display:none">"class_heads_weight"</span><span data-if="dotnet" style="display:none">"class_heads_weight"</span><span data-if="python" style="display:none">"class_heads_weight"</span></i>
</p>
</li>
<li>
<p> <i><span data-if="hdevelop" style="display:inline">'capacity'</span><span data-if="c" style="display:none">"capacity"</span><span data-if="cpp" style="display:none">"capacity"</span><span data-if="com" style="display:none">"capacity"</span><span data-if="dotnet" style="display:none">"capacity"</span><span data-if="python" style="display:none">"capacity"</span></i>
</p>
</li>
<li>
<p> <i><span data-if="hdevelop" style="display:inline">'class_ids'</span><span data-if="c" style="display:none">"class_ids"</span><span data-if="cpp" style="display:none">"class_ids"</span><span data-if="com" style="display:none">"class_ids"</span><span data-if="dotnet" style="display:none">"class_ids"</span><span data-if="python" style="display:none">"class_ids"</span></i>
</p>
</li>
<li>
<p> <i><span data-if="hdevelop" style="display:inline">'class_ids_no_orientation'</span><span data-if="c" style="display:none">"class_ids_no_orientation"</span><span data-if="cpp" style="display:none">"class_ids_no_orientation"</span><span data-if="com" style="display:none">"class_ids_no_orientation"</span><span data-if="dotnet" style="display:none">"class_ids_no_orientation"</span><span data-if="python" style="display:none">"class_ids_no_orientation"</span></i>
</p>
</li>
<li>
<p> <i><span data-if="hdevelop" style="display:inline">'class_names'</span><span data-if="c" style="display:none">"class_names"</span><span data-if="cpp" style="display:none">"class_names"</span><span data-if="com" style="display:none">"class_names"</span><span data-if="dotnet" style="display:none">"class_names"</span><span data-if="python" style="display:none">"class_names"</span></i>
</p>
</li>
<li>
<p> <i><span data-if="hdevelop" style="display:inline">'class_weights'</span><span data-if="c" style="display:none">"class_weights"</span><span data-if="cpp" style="display:none">"class_weights"</span><span data-if="com" style="display:none">"class_weights"</span><span data-if="dotnet" style="display:none">"class_weights"</span><span data-if="python" style="display:none">"class_weights"</span></i>
</p>
</li>
<li>
<p> <i><span data-if="hdevelop" style="display:inline">'freeze_backbone_level'</span><span data-if="c" style="display:none">"freeze_backbone_level"</span><span data-if="cpp" style="display:none">"freeze_backbone_level"</span><span data-if="com" style="display:none">"freeze_backbone_level"</span><span data-if="dotnet" style="display:none">"freeze_backbone_level"</span><span data-if="python" style="display:none">"freeze_backbone_level"</span></i>
</p>
</li>
<li>
<p> <i><span data-if="hdevelop" style="display:inline">'ignore_direction'</span><span data-if="c" style="display:none">"ignore_direction"</span><span data-if="cpp" style="display:none">"ignore_direction"</span><span data-if="com" style="display:none">"ignore_direction"</span><span data-if="dotnet" style="display:none">"ignore_direction"</span><span data-if="python" style="display:none">"ignore_direction"</span></i>
</p>
</li>
<li>
<p> <i><span data-if="hdevelop" style="display:inline">'image_dimensions'</span><span data-if="c" style="display:none">"image_dimensions"</span><span data-if="cpp" style="display:none">"image_dimensions"</span><span data-if="com" style="display:none">"image_dimensions"</span><span data-if="dotnet" style="display:none">"image_dimensions"</span><span data-if="python" style="display:none">"image_dimensions"</span></i>
</p>
</li>
<li>
<p> <i><span data-if="hdevelop" style="display:inline">'image_height'</span><span data-if="c" style="display:none">"image_height"</span><span data-if="cpp" style="display:none">"image_height"</span><span data-if="com" style="display:none">"image_height"</span><span data-if="dotnet" style="display:none">"image_height"</span><span data-if="python" style="display:none">"image_height"</span></i>, <i><span data-if="hdevelop" style="display:inline">'image_width'</span><span data-if="c" style="display:none">"image_width"</span><span data-if="cpp" style="display:none">"image_width"</span><span data-if="com" style="display:none">"image_width"</span><span data-if="dotnet" style="display:none">"image_width"</span><span data-if="python" style="display:none">"image_width"</span></i>
</p>
</li>
<li>
<p> <i><span data-if="hdevelop" style="display:inline">'image_num_channels'</span><span data-if="c" style="display:none">"image_num_channels"</span><span data-if="cpp" style="display:none">"image_num_channels"</span><span data-if="com" style="display:none">"image_num_channels"</span><span data-if="dotnet" style="display:none">"image_num_channels"</span><span data-if="python" style="display:none">"image_num_channels"</span></i>
</p>
</li>
<li>
<p> <i><span data-if="hdevelop" style="display:inline">'instance_segmentation'</span><span data-if="c" style="display:none">"instance_segmentation"</span><span data-if="cpp" style="display:none">"instance_segmentation"</span><span data-if="com" style="display:none">"instance_segmentation"</span><span data-if="dotnet" style="display:none">"instance_segmentation"</span><span data-if="python" style="display:none">"instance_segmentation"</span></i>
</p>
</li>
<li>
<p> <i><span data-if="hdevelop" style="display:inline">'instance_type'</span><span data-if="c" style="display:none">"instance_type"</span><span data-if="cpp" style="display:none">"instance_type"</span><span data-if="com" style="display:none">"instance_type"</span><span data-if="dotnet" style="display:none">"instance_type"</span><span data-if="python" style="display:none">"instance_type"</span></i>
</p>
</li>
<li>
<p> <i><span data-if="hdevelop" style="display:inline">'mask_head_weight'</span><span data-if="c" style="display:none">"mask_head_weight"</span><span data-if="cpp" style="display:none">"mask_head_weight"</span><span data-if="com" style="display:none">"mask_head_weight"</span><span data-if="dotnet" style="display:none">"mask_head_weight"</span><span data-if="python" style="display:none">"mask_head_weight"</span></i></p>
<p>
<i>Restriction:</i> only instance segmentation
</p>
</li>
<li>
<p> <i><span data-if="hdevelop" style="display:inline">'max_level'</span><span data-if="c" style="display:none">"max_level"</span><span data-if="cpp" style="display:none">"max_level"</span><span data-if="com" style="display:none">"max_level"</span><span data-if="dotnet" style="display:none">"max_level"</span><span data-if="python" style="display:none">"max_level"</span></i>, <i><span data-if="hdevelop" style="display:inline">'min_level'</span><span data-if="c" style="display:none">"min_level"</span><span data-if="cpp" style="display:none">"min_level"</span><span data-if="com" style="display:none">"min_level"</span><span data-if="dotnet" style="display:none">"min_level"</span><span data-if="python" style="display:none">"min_level"</span></i>
</p>
</li>
<li>
<p> <i><span data-if="hdevelop" style="display:inline">'max_num_detections'</span><span data-if="c" style="display:none">"max_num_detections"</span><span data-if="cpp" style="display:none">"max_num_detections"</span><span data-if="com" style="display:none">"max_num_detections"</span><span data-if="dotnet" style="display:none">"max_num_detections"</span><span data-if="python" style="display:none">"max_num_detections"</span></i>
</p>
</li>
<li>
<p> <i><span data-if="hdevelop" style="display:inline">'max_overlap'</span><span data-if="c" style="display:none">"max_overlap"</span><span data-if="cpp" style="display:none">"max_overlap"</span><span data-if="com" style="display:none">"max_overlap"</span><span data-if="dotnet" style="display:none">"max_overlap"</span><span data-if="python" style="display:none">"max_overlap"</span></i>
</p>
</li>
<li>
<p> <i><span data-if="hdevelop" style="display:inline">'max_overlap_class_agnostic'</span><span data-if="c" style="display:none">"max_overlap_class_agnostic"</span><span data-if="cpp" style="display:none">"max_overlap_class_agnostic"</span><span data-if="com" style="display:none">"max_overlap_class_agnostic"</span><span data-if="dotnet" style="display:none">"max_overlap_class_agnostic"</span><span data-if="python" style="display:none">"max_overlap_class_agnostic"</span></i>
</p>
</li>
<li>
<p> <i><span data-if="hdevelop" style="display:inline">'min_confidence'</span><span data-if="c" style="display:none">"min_confidence"</span><span data-if="cpp" style="display:none">"min_confidence"</span><span data-if="com" style="display:none">"min_confidence"</span><span data-if="dotnet" style="display:none">"min_confidence"</span><span data-if="python" style="display:none">"min_confidence"</span></i>
</p>
</li>
<li>
<p> <i><span data-if="hdevelop" style="display:inline">'optimize_for_inference'</span><span data-if="c" style="display:none">"optimize_for_inference"</span><span data-if="cpp" style="display:none">"optimize_for_inference"</span><span data-if="com" style="display:none">"optimize_for_inference"</span><span data-if="dotnet" style="display:none">"optimize_for_inference"</span><span data-if="python" style="display:none">"optimize_for_inference"</span></i>
</p>
</li>
</ul>
<h2 id="sec_attention">注意</h2>
<p>To successfully set <i><span data-if="hdevelop" style="display:inline">'gpu'</span><span data-if="c" style="display:none">"gpu"</span><span data-if="cpp" style="display:none">"gpu"</span><span data-if="com" style="display:none">"gpu"</span><span data-if="dotnet" style="display:none">"gpu"</span><span data-if="python" style="display:none">"gpu"</span></i> parameters, cuDNN and cuBLAS are
required, i.e., to set the parameter <code><span data-if="hdevelop" style="display:inline">GenParamName</span><span data-if="c" style="display:none">GenParamName</span><span data-if="cpp" style="display:none">GenParamName</span><span data-if="com" style="display:none">GenParamName</span><span data-if="dotnet" style="display:none">genParamName</span><span data-if="python" style="display:none">gen_param_name</span></code>
<i><span data-if="hdevelop" style="display:inline">'runtime'</span><span data-if="c" style="display:none">"runtime"</span><span data-if="cpp" style="display:none">"runtime"</span><span data-if="com" style="display:none">"runtime"</span><span data-if="dotnet" style="display:none">"runtime"</span><span data-if="python" style="display:none">"runtime"</span></i> to <i><span data-if="hdevelop" style="display:inline">'gpu'</span><span data-if="c" style="display:none">"gpu"</span><span data-if="cpp" style="display:none">"gpu"</span><span data-if="com" style="display:none">"gpu"</span><span data-if="dotnet" style="display:none">"gpu"</span><span data-if="python" style="display:none">"gpu"</span></i>.
For further details, please refer to the <code>“Installation Guide”</code>,
paragraph “Requirements for Deep Learning and Deep-Learning-Based Methods”.
</p>
<h2 id="sec_execution">运行信息</h2>
<ul>
  <li>多线程类型:可重入(与非独占操作符并行运行)。</li>
<li>多线程作用域:全局(可以从任何线程调用)。</li>
  <li>未经并行化处理。</li>
</ul>
<p>This operator returns a handle. Note that the state of an instance of this handle type may be changed by specific operators even though the handle is used as an input parameter by those operators.</p>
<h2 id="sec_parameters">参数表</h2>
  <div class="par">
<div class="parhead">
<span id="Backbone" class="parname"><b><code><span data-if="hdevelop" style="display:inline">Backbone</span><span data-if="c" style="display:none">Backbone</span><span data-if="cpp" style="display:none">Backbone</span><span data-if="com" style="display:none">Backbone</span><span data-if="dotnet" style="display:none">backbone</span><span data-if="python" style="display:none">backbone</span></code></b> (input_control)  </span><span>filename.read <code>→</code> <span data-if="dotnet" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="python" style="display:none">str</span><span data-if="cpp" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="c" style="display:none">Htuple</span><span data-if="hdevelop" style="display:inline"> (string)</span><span data-if="dotnet" style="display:none"> (<i>string</i>)</span><span data-if="cpp" style="display:none"> (<i>HString</i>)</span><span data-if="c" style="display:none"> (<i>char*</i>)</span></span>
</div>
<p class="pardesc">Deep learning classifier, used as backbone network.</p>
<p class="pardesc"><span class="parcat">Default:
      </span>
    <span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_compact.hdl'</span>
    <span data-if="c" style="display:none">"pretrained_dl_classifier_compact.hdl"</span>
    <span data-if="cpp" style="display:none">"pretrained_dl_classifier_compact.hdl"</span>
    <span data-if="com" style="display:none">"pretrained_dl_classifier_compact.hdl"</span>
    <span data-if="dotnet" style="display:none">"pretrained_dl_classifier_compact.hdl"</span>
    <span data-if="python" style="display:none">"pretrained_dl_classifier_compact.hdl"</span>
</p>
<p class="pardesc"><span class="parcat">List of values:
      </span><span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_alexnet.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_alexnet.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_alexnet.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_alexnet.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_alexnet.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_alexnet.hdl"</span>, <span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_compact.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_compact.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_compact.hdl"</span>, <span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_enhanced.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_enhanced.hdl"</span>, <span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_mobilenet_v2.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_mobilenet_v2.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_mobilenet_v2.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_mobilenet_v2.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_mobilenet_v2.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_mobilenet_v2.hdl"</span>, <span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_resnet18.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_resnet18.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_resnet18.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_resnet18.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_resnet18.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_resnet18.hdl"</span>, <span data-if="hdevelop" style="display:inline">'pretrained_dl_classifier_resnet50.hdl'</span><span data-if="c" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span><span data-if="cpp" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span><span data-if="com" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span><span data-if="dotnet" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span><span data-if="python" style="display:none">"pretrained_dl_classifier_resnet50.hdl"</span></p>
<p class="pardesc"><span class="parcat">File extension:
          </span>.<code>hdl</code></p>
</div>
  <div class="par">
<div class="parhead">
<span id="NumClasses" class="parname"><b><code><span data-if="hdevelop" style="display:inline">NumClasses</span><span data-if="c" style="display:none">NumClasses</span><span data-if="cpp" style="display:none">NumClasses</span><span data-if="com" style="display:none">NumClasses</span><span data-if="dotnet" style="display:none">numClasses</span><span data-if="python" style="display:none">num_classes</span></code></b> (input_control)  </span><span>integer <code>→</code> <span data-if="dotnet" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="python" style="display:none">int</span><span data-if="cpp" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="c" style="display:none">Htuple</span><span data-if="hdevelop" style="display:inline"> (integer)</span><span data-if="dotnet" style="display:none"> (<i>int</i> / </span><span data-if="dotnet" style="display:none">long)</span><span data-if="cpp" style="display:none"> (<i>Hlong</i>)</span><span data-if="c" style="display:none"> (<i>Hlong</i>)</span></span>
</div>
<p class="pardesc">Number of classes.</p>
<p class="pardesc"><span class="parcat">Default:
      </span>3</p>
</div>
  <div class="par">
<div class="parhead">
<span id="DLModelDetectionParam" class="parname"><b><code><span data-if="hdevelop" style="display:inline">DLModelDetectionParam</span><span data-if="c" style="display:none">DLModelDetectionParam</span><span data-if="cpp" style="display:none">DLModelDetectionParam</span><span data-if="com" style="display:none">DLModelDetectionParam</span><span data-if="dotnet" style="display:none">DLModelDetectionParam</span><span data-if="python" style="display:none">dlmodel_detection_param</span></code></b> (input_control)  </span><span>dict <code>→</code> <span data-if="dotnet" style="display:none"><a href="HDict.html">HDict</a>, </span><span data-if="dotnet" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="python" style="display:none">HHandle</span><span data-if="cpp" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="c" style="display:none">Htuple</span><span data-if="hdevelop" style="display:inline"> (handle)</span><span data-if="dotnet" style="display:none"> (<i>IntPtr</i>)</span><span data-if="cpp" style="display:none"> (<i>HHandle</i>)</span><span data-if="c" style="display:none"> (<i>handle</i>)</span></span>
</div>
<p class="pardesc">Parameters for the object detection model.</p>
<p class="pardesc"><span class="parcat">Default:
      </span>[]</p>
</div>
  <div class="par">
<div class="parhead">
<span id="DLModelHandle" class="parname"><b><code><span data-if="hdevelop" style="display:inline">DLModelHandle</span><span data-if="c" style="display:none">DLModelHandle</span><span data-if="cpp" style="display:none">DLModelHandle</span><span data-if="com" style="display:none">DLModelHandle</span><span data-if="dotnet" style="display:none">DLModelHandle</span><span data-if="python" style="display:none">dlmodel_handle</span></code></b> (output_control)  </span><span>dl_model <code>→</code> <span data-if="dotnet" style="display:none"><a href="HDlModel.html">HDlModel</a>, </span><span data-if="dotnet" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="python" style="display:none">HHandle</span><span data-if="cpp" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="c" style="display:none">Htuple</span><span data-if="hdevelop" style="display:inline"> (handle)</span><span data-if="dotnet" style="display:none"> (<i>IntPtr</i>)</span><span data-if="cpp" style="display:none"> (<i>HHandle</i>)</span><span data-if="c" style="display:none"> (<i>handle</i>)</span></span>
</div>
<p class="pardesc">Deep learning model for object detection.</p>
</div>
<h2 id="sec_result">结果</h2>
<p>如果参数均有效，算子 <code><span data-if="hdevelop" style="display:inline">create_dl_model_detection</span><span data-if="c" style="display:none">create_dl_model_detection</span><span data-if="cpp" style="display:none">CreateDlModelDetection</span><span data-if="com" style="display:none">CreateDlModelDetection</span><span data-if="dotnet" style="display:none">CreateDlModelDetection</span><span data-if="python" style="display:none">create_dl_model_detection</span></code>
返回值 <TT>2</TT> (
      <TT>H_MSG_TRUE</TT>)
    . 如有必要，将引发异常。</p>
<h2 id="sec_successors">可能的后置算子</h2>
<p>
<code><a href="set_dl_model_param.html"><span data-if="hdevelop" style="display:inline">set_dl_model_param</span><span data-if="c" style="display:none">set_dl_model_param</span><span data-if="cpp" style="display:none">SetDlModelParam</span><span data-if="com" style="display:none">SetDlModelParam</span><span data-if="dotnet" style="display:none">SetDlModelParam</span><span data-if="python" style="display:none">set_dl_model_param</span></a></code>, 
<code><a href="get_dl_model_param.html"><span data-if="hdevelop" style="display:inline">get_dl_model_param</span><span data-if="c" style="display:none">get_dl_model_param</span><span data-if="cpp" style="display:none">GetDlModelParam</span><span data-if="com" style="display:none">GetDlModelParam</span><span data-if="dotnet" style="display:none">GetDlModelParam</span><span data-if="python" style="display:none">get_dl_model_param</span></a></code>, 
<code><a href="apply_dl_model.html"><span data-if="hdevelop" style="display:inline">apply_dl_model</span><span data-if="c" style="display:none">apply_dl_model</span><span data-if="cpp" style="display:none">ApplyDlModel</span><span data-if="com" style="display:none">ApplyDlModel</span><span data-if="dotnet" style="display:none">ApplyDlModel</span><span data-if="python" style="display:none">apply_dl_model</span></a></code>, 
<code><a href="train_dl_model_batch.html"><span data-if="hdevelop" style="display:inline">train_dl_model_batch</span><span data-if="c" style="display:none">train_dl_model_batch</span><span data-if="cpp" style="display:none">TrainDlModelBatch</span><span data-if="com" style="display:none">TrainDlModelBatch</span><span data-if="dotnet" style="display:none">TrainDlModelBatch</span><span data-if="python" style="display:none">train_dl_model_batch</span></a></code>
</p>
<h2 id="sec_alternatives">可替代算子</h2>
<p>
<code><a href="read_dl_model.html"><span data-if="hdevelop" style="display:inline">read_dl_model</span><span data-if="c" style="display:none">read_dl_model</span><span data-if="cpp" style="display:none">ReadDlModel</span><span data-if="com" style="display:none">ReadDlModel</span><span data-if="dotnet" style="display:none">ReadDlModel</span><span data-if="python" style="display:none">read_dl_model</span></a></code>
</p>
<h2 id="sec_module">模块</h2>
<p>
Deep Learning Training</p>
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